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Hybrid multiscale modelling has emerged as a useful framework for modellingcomplex biological phenomena. However, when accounting for stochasticity in theinternal dynamics of agents, these models frequently become computationallyexpensive. Traditional techniques to reduce the computational intensity of suchmodels can lead to a reduction in the richness of the dynamics observed,compared to the original system. Here we use large deviation theory to decreasethe computational cost of a spatially-extended multi-agent stochastic systemwith a region of multi-stability by coarse-graining it to a continuous timeMarkov chain on the state space of stable steady states of the original system.Our technique preserves the original description of the stable steady states ofthe system and accounts for noise-induced transitions between them. We applythe method to a bistable system modelling phenotype specification of cellsdriven by a lateral inhibition mechanism. For this system, we demonstrate howthe method may be used to explore different pattern configurations and unveilrobust patterns emerging on longer timescales. We then compare the fullstochastic, coarse-grained and mean-field descriptions via patternquantification metrics and in terms of the numerical cost of each method. Ourresults show that the coarse-grained system exhibits the lowest computationalcost while preserving the rich dynamics of the stochastic system. The methodhas the potential to reduce the computational complexity of hybrid multiscalemodels, making them more tractable for analysis, simulation and hypothesistesting.

A method to coarse-grain multi-agent stochastic systems with regions of multistability